Radiation treatment planning involves complex decision making in specifying the best possible treatment criteria and designing treatment parameters that take into account all aspects of patient conditions and treatment constraints. When physicians specify the best possible treatment criteria that includes the prescription to the target, dose sparing criteria for each of the critical organs at risk (OARs), they combine their personal experience and knowledge, published guidelines, and patient's specific condition into numerical specifications for the radiation treatment to be designed. For the given treatment criteria, physicians and planners try to design a treatment plan that can best possibly meet these criteria. The design includes a set of parameters, such as beam angles, beam ranges, beam energies, beam sizes, dose limits or volume limits, and associated priorities for sparing various organs or anatomy structures, that can be used in determining the treatment plan and the treatment dose distribution. Once the parameters that best meet the treatment criteria are determined, a high quality treatment plan can be manually or automatically generated that leads to highest quality radiation treatment for the specific patient. For cases in the thorax, abdomen and upper pelvic, as well as the brain, the selection of the incident angles of the treatment beams is a critical component of designing planning parameters. Current practice of selection of best beam angles or beam ranges for a specific case largely relies on personal experience and knowledge.
Previous techniques include methods for beam configuration determination. These techniques can be divided into three approaches. In one approach, one or more beam configuration templates (beam bouquets) are determined from prior clinical plans and are then applied to new cases that fit the general characteristics of the tumor (such as location, cancer type, and the like). This approach leverages prior expert knowledge and experience, but does not consider unique information of the new patient. In the second approach, physics-based principles and anatomical information are used to find the most efficient beam pathways in the existence of tumor and critical organs. These solutions leverage unique anatomical information of a new patient, they are efficient, but they rely on some fixed, general assumptions that may not cover all clinical needs. In the third approach, direct dose optimization methods, such as simulated annealing, genetic algorithm, sparse optimization, nested partition, pattern search and column generation are used to determine a set of individual beam angle positions. These solutions can be computationally very expensive and therefore often require heuristic approximations. For example, two of the previous solutions choose to iteratively add or subtract one beam at a time until a local optimum is reached. The subtraction method starts with a set of most frequently used beams determined from a collection of prior plans. Most of these previous solutions do not handle non-coplanar beam angles and do not consider prior knowledge.
For the aforementioned reasons, current practice of selection of best beam angles or beam ranges for a specific case largely relies on personal experience and knowledge. Thus, there is a desire to provide systems and techniques that can efficiently and automatically determine beam configurations that are not only based on the best available clinical experience and knowledge but also specialized to a patient's unique anatomical and clinical conditions.